While rule-based approaches mostly rely upon lexical information and require no training, machine learning invariably makes use of training data and exploits different types of information sources (or features), such as bags of words, syntactic patterns, sentiment information or semantic relatedness. Earliest attempts in this line use similarity between word embeddings as features for sarcasm detection. [1]} use a combination of convolutional neural networks, LSTM followed by a DNN. [2]} propose a model that identifies sarcastic tweets and subsequently differentiates the type (out of four classes) of expressed sarcasm. The systems that were submitted for both subtasks represent a variety of neural-network-based approaches (i.e., CNNs, RNNs, and (bi-)LSTMs) exploiting word and character embeddings as well as handcrafted features.
